Combining binary constraint networks in qualitative reasoning

Li, JJ; Kowalski, T; Renz, J; Li, S

Combining binary constraint networks in qualitative reasoning

Li, JJ Kowalski, T Renz, J Li, S

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Publication Type:: Conference Proceeding
Citation:: Frontiers in Artificial Intelligence and Applications, 2018, 178 pp. 515 - 519
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Li, JJ	en_US
dc.contributor.author	Kowalski, T	en_US
dc.contributor.author	Renz, J	en_US
dc.contributor.author	Li, S https://orcid.org/0000-0002-3332-2546	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Frontiers in Artificial Intelligence and Applications, 2018, 178 pp. 515 - 519	en_US
dc.identifier.issn	0922-6389	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10821
dc.description.abstract	© 2008 The authors and IOS Press. All rights reserved. Constraint networks in qualitative spatial and temporal reasoning are always complete graphs. When one adds an extra element to a given network, previously unknown constraints are derived by intersections and compositions of other constraints, and this may introduce inconsistency to the overall network. Likewise, when combining two consistent networks that share a common part, the combined network may become inconsistent. In this paper, we analyse the problem of combining these binary constraint networks and develop certain conditions to ensure combining two networks will never introduce an inconsistency for a given spatial or temporal calculus. This enables us to maintain a consistent world-view while acquiring new information in relation with some part of it. In addition, our results enable us to prove other important properties of qualitative spatial and temporal calculi in areas such as representability and complexity.	en_US
dc.relation.ispartof	Frontiers in Artificial Intelligence and Applications	en_US
dc.relation.isbasedon	10.3233/978-1-58603-891-5-515	en_US
dc.title	Combining binary constraint networks in qualitative reasoning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	178	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Patras, Greece	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	178	en_US

Abstract:

© 2008 The authors and IOS Press. All rights reserved. Constraint networks in qualitative spatial and temporal reasoning are always complete graphs. When one adds an extra element to a given network, previously unknown constraints are derived by intersections and compositions of other constraints, and this may introduce inconsistency to the overall network. Likewise, when combining two consistent networks that share a common part, the combined network may become inconsistent. In this paper, we analyse the problem of combining these binary constraint networks and develop certain conditions to ensure combining two networks will never introduce an inconsistency for a given spatial or temporal calculus. This enables us to maintain a consistent world-view while acquiring new information in relation with some part of it. In addition, our results enable us to prove other important properties of qualitative spatial and temporal calculi in areas such as representability and complexity.

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http://hdl.handle.net/10453/10821